Rendering snow : a light transport model for compressed anisotropic granular media

Mackenzie, Kevin
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Stewart, Charles V.
Patterson, Stacy
Cutler, Barbara M.
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Computer science
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Snow is a complex material that can take on many different visual properties based on the structure and shape of the individual ice grains that compose it. Individual ice grains dictate the surface texture and the transport of light through the medium as an aggregate, but simulating at this detail is computationally expensive. Existing methods for rendering granular media address this cost by approximating light transport through randomly oriented grains and only use specific individual grains to resolve surface-level details. However, they assume that grains are approximated well as a packing of non-overlapping bounding spheres, which is not always the case for snow (i.e. the classic snowflake shape). I present a light transport model that relaxes the non-overlapping requirement and enables compressed packings for highly non-spherical, anisotropic, grain shapes. It can produce snow objects that are progressively denser in appearance, both in surface detail and light transport, from sparse packings to highly compressed packings across several grain types. This model and the geometric basis I establish for compressed packings is also promising for extending the state-of-the-art granular media framework to support compressed packings of anisotropic grains.
August 2021
School of Science
Dept. of Computer Science
Rensselaer Polytechnic Institute, Troy, NY
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